Correction of the Soil Adjusted Factors of Vegetation Indices for Monitoring Rice Leaf Index Zrea

The vegetation indices that take the soil adjustment factor into consideration can reduce the influence of soil background conditions and have been widely used in monitoring all kinds of vegetation.However,the rice has been planted in the soil covered by a certain thickness of layer of water,which is different with other various soil backgrounds.Therefore,in this paper,through two years of rice plot experiments,we obtained the rice canopy spectral data and the corresponding leaf area index (LAI) data,and then calculated a series of vegetation indices (EVI,SAVI,WDVI) by using different soil adjustment factors changing within a certain range.We compared the abilities of these vegetation indices for rice LAI estimation,and then determine the optimum soil adjustment factors of vegetation indices to adjust the background of rice.In the study,we found that the best soil adjustment factor L for EVI,L of SAVI,a of WDVI are 0.25,0.10 and 1.25 respectively,and we further compared the LAI estimation results of the best soil adjustment factor with those of the conventional soil adjustment factor.For the model taking EVI as an independent variable,the RMSE of LAI estimation using the best soil adjustment factor is 6.82 % lower than that using the conventional soil adjustment factor;In SAVI model,the RMSE using the best soil adjustment factor is 10.23% lower than that using the conventional soil adjustment factor .These results indicate that the corrected vegetation indices considering the background of rice can improve the accuracy of rice leaf area index using remotely sensed data.     

主被动遥感数据协同估算干旱区草原植被生物量

结合主动微波遥感和被动光学遥感反映地表植被的各自优势,发展了一种主被动遥感协同估算干旱区草原植被生物量的模型。该模型将植被覆盖度作为水云模型的附加参数,将总体散射分为植被覆盖区散射和裸土区散射两部分,将水云模型应用到了植被覆盖稀疏区域。利用改进的水云模型和双极化ASAR数据,通过建立方程组估算植被生物量。将该方法用于乌图美仁草原植被生物量的估算,验证了该方法的有效性。结果表明:该主被动遥感协同估算模型能够成功地估算干旱区草原植被生物量,并且取得了较好的估算精度(R2=0.8562,RMSE=0.1813kg/m2)。最后,分析了该方法估算植被生物量的误差来源。     

A modified vegetation backscattering model for leaf area index retrieval from SAR time series

In this study, a semi-empirical modified vegetation backscattering model was developed to retrieve leaf area index (LAI) based on multi-temporal Radarsat-2 data and ground observations collected in China. This model combined the contribution of the vegetation and bare soil at the pixel level by adding vegetation coverage and the influence of bare soil on the total backscatter coefficients. Then, a lookup table algorithm was applied to calculate the value of vegetation water content and retrieve the LAI based on the linear relationship between the vegetation water content and LAI. The results indicated that the modified model was effective in evaluating and reproducing the total backscatter coefficients. Meanwhile, the LAI retrieval was well conducted with coefficient of determination (R²) and root mean square error (RMSE) of 89% and 0.19 m² m^?2, respectively. Additionally, this method offers insight into the required application accuracy of LAI retrieval in the agricultural regions.     

Multiyear independent validation of the water cloud model for retrieving maize leaf area index from SAR time series

Taking advantage of a large multiyear data set of synthetic aperture radar (SAR) and ground observations collected in Belgium, this research aims at improving the understanding of the SAR signal sensitivity to crop growth by means of water cloud model (WCM) inversion for retrieving maize leaf area index (LAI) from C-band and VV-polarized SAR data. The results show that at intermediate moisture levels, the contributions of both soil and plants to the SAR response are confused as, to the SAR sensor, the vegetation seems to behave as bare soil of about 21% water content. Moreover, as the WCM usually required a calibration every year, this research assessed the robustness of the calibrated WCM by model cross-validation between years for maize. Ten different calibrations and inversions of the WCM were completed based on three years of observations. Two other years of observation serve as independent data sets to calculate the LAI retrieval error. The results demonstrate the capability of transferring the model calibration to independent subsequent crop seasons with an acceptable performance reduction.     

西南地区不同山地环境梯度叶面积指数遥感反演

叶面积指数(LAI)遥感估算是植被定量遥感研究的热点之一,监测植被LAI时空变化对于研究陆地生态系统碳循环及全球变化等具有非常重要的意义。在我国西南山区设置10个50km×50km的观测样区作为研究区,其中包括5个森林生态系统样区、3个农田生态系统样区和2个草地生态系统样区。分别获取不同优势植被类型LAI地面实测数据,结合同期获取的遥感数据,考虑地形因素影响,基于偏最小二乘原理分别构建各样区LAI遥感估算模型,并采用交叉验证的方式对模型精度进行评价。结果表明:考虑了海拔、坡度和坡向等地形因子的森林LAI遥感反演模型与未考虑地形变量的模型相比,其验证精度有所提高,R2由0.30~0.75提高至0.50~0.80,RMSE由0.52~0.93m2/m2降低至0.48~0.89m2/m2;所有样区优势植被类型LAI反演模型验证R2在0.40~0.80之间,RMSE在0.22~0.89m2/m2之间。发展的LAI遥感估算方法有助于认知山地植被LAI反演的地形效应问题,可为进一步的山地植被长势监测提供科学依据。     

联合无人机激光雷达和高光谱数据反演玉米叶面积指数

叶面积指数（Leaf Area Index,LAI）是作物长势监测及产量估算的重要指标,准确高效的LAI反演对农田经济的宏观管理具有重要作用。研究探索了联合无人机激光雷达（Light Detection and Ranging,LiDAR）和高光谱数据反演玉米叶面积指数的潜力,并分析了LiDAR数据不同采样尺寸、高度阈值、点密度对LAI反演精度的影响同时确定三者的最优值。该研究分别从重采样的LiDAR数据和高光谱影像中提取了LiDAR变量和植被指数,然后基于偏最小二乘回归（Partial Least Square Regression,PLSR）和随机森林（Random Forest,RF）回归两种算法分别利用LiDAR变量、植被指数、联合LiDAR变量和植被指数构建预测模型,并确定反演玉米LAI的最优预测模型。结果表明：反演玉米LAI的最优采样尺寸、高度阈值、点密度分别为5.5 m、0.55 m、18 points/m2,研究发现最高的点密度（420 points/m2）并没有产生最优的玉米LAI反演精度,因此单独依靠增加点密度的方法提高LAI的反演精度并不可靠。基于LiDAR变量获...     

Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data

This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect,the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model,and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally,the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province,and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface,the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data,the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results,and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm³/cm³.     

Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data

The first year of Moderate Resolution Imaging Spectroradiometer (MODIS) data are compared with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data for derivation of biophysical variables in Senegal, West Africa. The dynamic range of the two MODIS vegetation indices (VIs)—the continuity vegetation index (CVI) and the enhanced vegetation index (EVI)—is generally much larger than for the NOAA AVHRR normalized difference vegetation index (NDVI) data, indicating the importance of the change in near-infrared wavelength configuration from the NOAA AVHRR sensor to the MODIS sensor. Senegal is characterized by a pronounced gradient in the vegetation density covering a range of agro-climatic zones from arid to humid and it is found that the MODIS CVI values saturate for high VI values while the EVI demonstrates improved sensitivity for high biomass. Compared to NOAA AVHRR the MODIS VIs generally correlate better to the MODIS fraction of absorbed photosynthetically active radiation (fAPAR) absorbed by vegetation canopies and the leaf area index (LAI; the one-sided green leaf area per unit ground area). CVI is found to correlate better to both fAPAR and LAI than is the case for EVI because of the larger dynamic range of the CVI data. This suggests that the problem of background contamination on VIs from soil is not as severe in Senegal as has been found in other semi-arid African areas.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Analysis of in-situ soil moisture data and validation of SMOS soil moisture products at selected agricultural sites over a tropical region

Calibration and validation activities on Soil Moisture and Ocean Salinity (SMOS)-derived soil moisture products have been conducted worldwide since the data became available, but this has not been the case over tropical regions. This study focuses on the setting up of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia and on the validation of SMOS soil moisture products. The in-situ data over a one-and-a-half-year period was analysed and the validation of the SMOS soil moisture products with this in-situ data was conducted. Bias and root mean square error (RMSE) were computed between the SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing times (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with a vegetation water content higher than 5 kg m^?2, an overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias ranged from 0.064 to 0.119 m³ m^?3 and the RMSE was from 0.090 to 0.158 m³ m^?3, when both ascending and descending mode data were measured. This RMSE was found to be similar to those of a number of studies conducted previously at different regions. However, a wet bias was found during the validation, while previous validation activities at other locations showed dry biases. The result of this study is useful to support the continuous development and improvement of the SMOS soil moisture retrieval model, aiming to produce soil moisture products with higher accuracy, especially in tropical regions.     

A proposed extension to the soil moisture and ocean salinity level 2 algorithm for mixed forest and moderate vegetation pixels

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km. Uncertainty in the retrieval of soil moisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soil moisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops), with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE, −3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, −0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.     

Leaf area index for biomes of the Eastern Arc Mountains: Landsat and SPOT observations along precipitation and altitude gradients

Understanding of mechanisms underlying carbon flux dynamics in the Eastern Arc Mountains and their catchment areas is lacking, due to data shortage (e.g. biome specific canopy structure) and spatial heterogeneity of tropical ecosystems. This study focuses on documenting leaf area index (LAI) for the main biomes in the Eastern Arc Mountains and their surroundings. In situ optical instruments, i.e. hemispherical photography and a SunScan device, were used to acquire ground LAI measurements. Spectral vegetation indices (VIs) extracted from Landsat Enhanced Thematic Mapper (ETM +) and Système Probatoire d'Observation de la Terre (SPOT) reflectance data were used, along with mean annual precipitation (MAP), as explanatory variables of LAI variation. The results indicate that LAI significantly increases with increasing MAP for woody biomes. Implementing long-term MAP as a second predictor variable into the VI–LAI models significantly improved LAI predictions by up to 10% using the normalised difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI 2) and 2-band enhanced vegetation index (EVI 2). Varying forest disturbances and agricultural management practises may have contributed to observed discrepancies of LAI with MAP across biomes. The importance of altitudinal gradients is yet to be explained fully with more study required. However, LAI appears to be higher in low-altitude forests compared to forests at higher altitudes. Our results indicate that SPOT and Landsat-derived VIs, in combination with long-term MAP, may be a suitable tool to develop landscape maps of LAI in Eastern Africa. This study also presents the in situ LAI measurements for further validation of global products for areas that are currently under-represented in Earth Observation (EO) global validation networks.     

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r ² = 0.61 and r ² = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r ² = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.     

The application of compact polarimetric decomposition algorithms to L-band PolSAR data in agricultural areas

ABSTRACT

In this paper, the applicability of the recently developed compact polarimetric decomposition and inversion algorithm to estimate soil moisture under low agricultural vegetation cover is investigated using simulated L-band compact polarimetric synthetic aperture radar (PolSAR) data. The surface scattering component is separated from the volume component of the vegetation through a model-based compact polarimetric decomposition (m-α) under the assumption of randomly orientated vegetation volume and reflection symmetry. The extracted surface scattering component is compared with two physics-based, low frequency surface scattering models such as extended Bragg (X-Bragg) and polarimetric two scale model (PTSM) in order to invert soil moisture for corresponding model- and data-derived surface scattering mechanism parameter α_s. In addition to the parameter α_s from m-α decomposition, the applicability of other scattering mechanism parameters, such as δ (relative phase) and χ (degree of circularity) from m-δ and m-χ decompositions are also investigated for their suitability to invert soil moisture. The algorithm is applied on a time series of simulated L-band compact polarimetric E-SAR data from the AgriSAR’2006 campaign over the Görmin test site in Northern Germany. The compact PolSAR-derived soil moisture is validated against in situ time-domain reflectometry (TDR) measurements. Including various growth stages of three different crop types, the estimated soil moisture values indicate an overall root mean square error (RMSE) of 9–12 and 9–15 vol.% using the X-Bragg model and the PTSM, respectively. The inversion rate for vegetation covered soils ranges from 5% to 40% including all phenological stages of the crops and different soil moisture conditions (range from 4 to 34 vol.%). The time series of soil moisture inversion results using compact polarimetry reveal that the developed algorithm is less sensitive to wet soils under growing agriculture crops due to less sensitivity of scattering mechanism parameters α_s and χ for ε_s > 20. Thus, further developments and investigations are needed to invert soil moisture for compact PolSAR data with high inversion rates and consistently less RMSE (<5 vol.%) over the various crop growing season.     

Errors associated with estimating vegetation water content from radar for use in passive microwave brightness temperature algorithms

The Soil Moisture Active Passive Validation Experiment 2012 was conducted as a pre-launch validation campaign for the Soil Moisture Active Passive mission over 6 weeks in June and July 2012. During this campaign, the Passive Active L-Band System (PALS) was flown at a low altitude, providing radar and radiometer measurements that were contained within a single agricultural field. The campaign domain consisted of 55 agricultural fields, where soil moisture was measured coincident to the PALS flight times and measurements of vegetation volumetric water content (VWC) and leaf area index (LAI) were measured weekly. The low-altitude flights allowed for the comparison between measured VWC and LAI for 11 fields to radar parameters derived from the radar backscatter. Only the correlation between the HV backscatter and the soybean VWC was considered strong (|r| > 0.7). All other correlations between the radar parameters and the VWC (or LAI) were moderate (0.3 < |r| < 0.7) or weak (|r|< 0.3). The established relationships between radar parameters and VWC were used in a forward radiation transfer model to estimate H-pol brightness temperature. It was found that the RMSE between the brightness temperatures estimated using the measured VWC was lowest when using the relationship between VWC and LAI (3.9 K for soybeans, 6.8 K for spring wheat, and 9.3 K when all crop data are combined). Despite a lower correlation, the RMSE associated with using the radar vegetation index relationship with VWC was less than when HV was used (7.9 K) for soybeans, which would result in an error in soil moisture estimation of just over 4%. The RMSEs for all other VWC and radar parameter relationships were greater than 10 K.     

Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation

Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)–Crop Environment Resource Synthesis (CERES)–Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM–CERES–Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and ?13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data.     

Integrating two layers of soil moisture parameters into the MOD16 algorithm to improve evapotranspiration estimations

To integrate soil moisture into the algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS) global evapotranspiration (ET) project (MOD16), two improvements were implemented: two layers of relative soil moisture parameters were combined with a surface resistance model; and the complementary relationship was replaced with the Penman-Monteith (P-M) method to estimate the dry soil surface evaporation. In the vegetation surface resistance model, a multiplier R_sm1 was added, and the influence of the relative soil moisture in the root zone was accounted for. In the soil surface resistance model, an empirical exponential relationship was used. To calculate the relative soil moisture parameters, soil hydraulic parameters, such as field capacity (Fc), wilting point (Wp), and saturation point (Sp), were estimated according to the soil texture information; these parameters were used as critical values to estimate the relative soil moisture. Both the MOD16 method and improved method were validated using ET flux data collected at nine flux-tower sites in the USA from 2000 to 2009. The mean absolute BIAS and the root mean square error (RMSE) decreased from 0.36 to 0.30 mm day^–1 and from 1.14 to 0.97 mm day^–1, respectively, after integrating the soil moisture parameters. Meanwhile, the mean correlation coefficient (R) for the nine sites increased from 0.54 to 0.70. Therefore, the improved method performed better than the MOD16 method. Furthermore, the uncertainties associated with the MODIS leaf area index (LAI) products, flux-tower measurements, soil texture, soil moisture, and model parameters were analysed. The outlook for future modifications was also discussed.     

基于LST-EVI特征空间的土壤水分含量反演

干旱是人类历史上的重大自然灾害之一,而土壤水分是干旱监测最重要的指标。利用遥感手段反演地表土壤水分,可以充分反映土壤水分的时空变化特征,适合进行大范围动态监测。研究基于Landsat TM数据,运用普适性单通道算法得到地表温度(LST,Land Surface Temperature),然后选用增强型植被指数(EVI,Enhanced Vegetation Index),构建了LST\|EVI特征空间,计算出温度植被干旱指数(TVDI,Temperature\|Vegetation Dryness Index)。在对实测土壤含水量数据和对应TVDI值进行回归分析的基础上,反演出2010年6月14日黄骅市自然地表20 cm深度处的体积含水量。结果表明:TVDI方法在该研究区是完全可行的,拟合精度较高;研究区自然地表土壤体积含水量分布差异明显,中等含水量地区面积最大,西南和部分北部地区含水量较低,而含水量高的区域主要分布在苇洼和沿海地区。     

Mapping tree distribution and LAI in peatlands using field methods and imaging spectroscopy: a case study for the Haaksbergerveen,the Netherlands

Leaf area index (LAI) is among the vegetation parameters that play an important role in climate, hydrological and ecological studies, and is used for assessing growth and expansion of vegetation. The main objective of this study was to develop a methodology to map the LAI distribution of birch trees (Betula pendula) in peatland ecosystems using field-based instruments and airborne-based remote-sensing techniques. The developed mapping method was validated using field-based LAI measurements using the LAI-2000 instrument. First vegetation indices, including simple ratio (SR), normalized difference vegetation index (NDVI), and reduced simple ratio (RSR), were derived from HyMap data and related to ground-based measurements of LAI. LAI related better with RSR (R² = 0.68), followed by NDVI (R² = 0.63) and SR (R² = 0.58), respectively. Areas with birch were identified using Spectral Angle Mapper (SAM) to classify the image into 11 end members of dominant species including bare soil and open water. Next, the relationship between LAI and RSR was applied to areas with birch, yielding a birch LAI map. Comparison of the map of the birch trees and field-based LAI data was done using linear regression, yielding an R² = 0.38 and an RMSE = 0.25, which is fairly accurate for a structurally highly diverse field situation. The method may prove an invaluable tool to monitor tree encroachment and assess tree LAI in these remote and poorly accessible areas.     

Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery

Vegetation water content is an important parameter for retrieval of soil moisture from microwave data and for other remote sensing applications. Because liquid water absorbs in the shortwave infrared, the normalized difference infrared index (NDII), calculated from Landsat 5 Thematic Mapper band 4 (0.76-0.90 μm wavelength) and band 5 (1.55-1.65 μm wavelength), can be used to determine canopy equivalent water thickness (EWT), which is defined as the water volume per leaf area times the leaf area index (LAI). Alternatively, average canopy EWT can be determined using a landcover classification, because different vegetation types have different average LAI at the peak of the growing season. The primary contribution of this study for the Soil Moisture Experiment 2004 was to sample vegetation for the Arizona and Sonora study areas. Vegetation was sampled to achieve a range of canopy EWT; LAI was measured using a plant canopy analyzer and digital hemispherical (fisheye) photographs. NDII was linearly related to measured canopy EWT with an R² of 0.601. Landcover of the Arizona, USA, and Sonora, Mexico, study areas were classified with an overall accuracy of 70% using a rule-based decision tree using three dates of Landsat 5 Thematic Mapper imagery and digital elevation data. There was a large range of NDII per landcover class at the peak of the growing season, indicating that canopy EWT should be estimated directly using NDII or other shortwave-infrared vegetation indices. However, landcover classifications will still be necessary to obtain total vegetation water content from canopy EWT and other data, because considerable liquid water is contained in the non-foliar components of vegetation.